K-means算法聚类中心选取

传统K-means 算法对于聚类初始点的选取和距离度量的计算异常敏感，因而很可能导致K-means 算法只能收敛得到局部最优解。为此，提出一种改进的K-means 算法，即K-means 聚类算法最优匹配算法，并进行了相关的算法实验分析。该改进算法首先对传统的K-means 聚类算法进行初始点的选取，并分析聚类结果。然后，分别从初始聚类中心的选择和距离算法的确定进行实验测试，引入轮廓系数评价聚类效果，分析实验结果可知，K-means 聚类算法最优匹配算法具有较好的稳定性和较高的聚类准确率。

Clustering Center Selection on K-means Clustering Algorithm

The traditional K-means algorithm is very sensitive to the selection of cluster initial points and the calculation of distance metrics，it is possible that the K-means algorithm can converge to local optimal solutions．Aiming at this problem，an improved K-means algorithm，namely K-means clustering algorithm optimal matching algorithm． The improved algorithm firstly selects the initial point of the traditional K-means clustering algorithm and analyzes the clustering result． Then，it conducts experimental tests from the selection of the initial clustering center and the determination of the distance algorithm respectively，and introduces the contour coefficient evaluation clustering． The experimental results prove that the optimal matching algorithm of K-means clustering algorithm has better stability and higher clustering accuracy．